Note: Intro -- Foreword -- Preface -- Contents -- 1 Intelligent Computing Systems -- 2 Semantic Tools -- Their Use for Knowledge Management in the Public Sector -- Abstract -- 1 Outlines -- 2 Introduction---Presentation of the Field of Interest -- 2.1 E-Government---The Opportunities Through the Semantic Web -- 2.2 Public Open Data for the Transition to `Open Government' -- 3 Related Work -- 4 Semantic Representation of Knowledge -- 4.1 The RDF Data Model -- 4.2 The URI's Use -- 4.3 RDF Schema Specification Language -- 4.4 Web Ontology Language---OWL -- 5 Reasoning Tools -- 5.1 SWRL Rules -- 5.2 The Query Language SQWRL -- 6 Presentation of Our Ontology Through Prot00E9g00E9 -- 6.1 The Ontology Development in Prot00E9g00E9 4.3 -- 6.2 The E-Government Ontology -- 6.2.1 Defining Classes -- 6.2.2 Defining Properties -- 6.3 The Use of RDF, RDFS, OWL and SWRL Through a Case Study -- 7 Data Mining Technology from Ontologies -- 7.1 SPARQL -- 7.2 SPARQL-DL in OWL2 Query Tab of Prot00E9g00E9 -- 7.3 DL Query Tool of Prot00E9g00E9 -- 8 Evaluation of Ontology -- 8.1 Categorization of the Ontology -- 8.2 Basic Principles of Design -- 8.3 Methodology of the Ontology Development -- 9 Conclusions -- References -- 3 From Game Theory to Complexity, Emergence and Agent-Based Modeling in World Politics -- Abstract -- 1 Introduction -- 2 Game Theory in World Politics -- 2.1 A Game Theoretic Approach of Global Environmental Diplomacy -- 3 From Game Theory to Complexity -- 3.1 Emergence in World Politics -- 4 Simulating Complexity with Agent-Based Modeling -- 4.1 Agent-Based Modeling Research in World Politics -- 4.1.1 Political Applications of ABM -- 5 Conclusions -- Acknowledgments -- References -- List of Software Resources -- 4 A Semantic Approach for Representing and Querying Business Processes -- Abstract -- 1 Introduction. , 2 Semantic Web Techniques in Management Information Systems -- 2.1 What's Worth in Combining Management Information Systems with Semantic Web Technologies? -- 2.2 Process Models, Conceptual Models and Ontologies -- 2.3 Querying Business Process Models -- 2.4 Related Work -- 3 A BPMN Semantic Process Model -- 3.1 The Research Methodology -- 3.2 Developing Business Process Models -- 3.3 Developing the Ontology -- 3.3.1 The Scope of the BPMN Elements -- 3.3.2 The Scope of the Generic BPMN Alternative Models -- 3.3.3 The Scope of the Agent or Actor Participating in the Process -- 3.4 Validating the Ontology -- 4 Querying Conventional Databases and Semantic Models -- 5 Conclusions -- References -- 5 Using Conversational Knowledge Management as a Lens for Virtual Collaboration in the Course of Small Group Activities -- Abstract -- 1 Introduction -- 2 Related Work and Motivation -- 2.1 Conversational Patterns -- 2.2 Design Frames and Technologies for CK Management -- 2.3 Consolidation and Research Focus -- 3 Methodology -- 3.1 Data Samples and Analysis -- 3.2 Language-Action Models -- 4 Implementation -- 4.1 Transformable Document Templates -- 4.2 The Portlets -- 5 Concluding Remarks -- Acknowledgment -- References -- 6 Spatial Environments for m-Learning: Review and Potentials -- Abstract -- 1 Introduction -- 2 List of Resources -- 3 Classification Criteria -- 4 Exemplary Environments -- 5 Comparison -- 6 Results -- 7 Conclusions/Future Work -- References -- 7 Science Teachers' Metaphors of Digital Technologies and Social Media in Pedagogy in Finland and in Greece -- Abstract -- 1 Introduction -- 2 Theoretical Background -- 2.1 Approaching Science -- 2.2 The Relationship Between Science and Digital Technology -- 3 The Study -- 3.1 Aims & -- Methods -- 3.2 The Context and the Participants -- 4 Findings -- 4.1 Science as Way of Thinking. , 4.2 Science as Method -- 5 Conclusions -- References -- 8 Data Driven Monitoring of Energy Systems: Gaussian Process Kernel Machine for Fault Identification with Application to Boiling Water Reactors -- Abstract -- 1 Introduction -- 2 Gaussian Process Kernel Machines -- 3 Methodology -- 4 Application to Monitoring Complex Energy Systems: The Boiling Water Reactor (BWR) Case -- 4.1 Problem Statement -- 4.2 Testing Results -- 5 Conclusions -- References -- 9 A Framework to Assess the Behavior and Performance of a City Towards Energy Optimization -- Abstract -- 1 Introduction -- 2 Policy Context -- 3 Current Relevant Initiatives -- 4 Description of the Framework -- 5 Municipal Building Level SCEAF -- 6 Conclusions -- Acknowledgment -- References -- 10 An Energy Management Platform for Smart Microgrids -- Abstract -- 1 Introduction -- 2 The Smart Polygeneration Microgrid Pilot Plant -- 3 The Energy Management Platform -- 4 The Supervisory, Control and Data Acquisition (SCADA) System -- 5 Results and Discussion -- 6 Conclusions and Future Research Lines -- References -- List of Resources -- 11 Transit Journaling and Traffic Sensitive Routing for a Mixed Mode Public Transportation System -- Abstract -- 1 Introduction -- 1.1 Limited Scope of Data -- 1.2 Formal Route Names Versus Informal Headsigns -- 1.3 Insufficient Stop Descriptions -- 1.4 Traffic Sensitivity in Routing/Trip Planning -- 2 Related Work -- 2.1 Crowdsourced Mapping and Real-time Tracking -- 2.2 Activity Detection -- 2.3 Trip Planning/Routing -- 2.3.1 Dijkstra's Algorithm -- 2.3.2 A* Search -- 2.3.3 Raptor -- 2.4 Trip Planning with Real-time Data -- 3 Methodology/Design -- 3.1 The Server/Back-End -- 3.1.1 GTFS Data Pre-processing -- 3.1.2 Server Design -- 3.1.3 The Modified RAPTOR Search Algorithm -- 3.2 The Mobile App -- 3.2.1 Search -- 3.2.2 Results/Journey Displays -- 3.2.3 Recording. , 3.2.4 Traffic Report -- 3.2.5 Results Display -- 3.2.6 Journey Display -- 3.2.7 Journal -- 3.2.8 Stop Editor -- 3.2.9 Route Editor -- 4 Tests and Results -- 4.1 Basic Routing Capacity -- 4.1.1 Survey -- 4.1.2 Demographics -- 4.1.3 Algorithm Evaluation -- 4.2 Traffic Sensitivity -- 4.3 Journey Recorder -- 5 Future Work -- 5.1 Base Estimate Correction -- 5.2 Preference-Weighing System -- 5.3 Traffic Flow Prediction -- 5.4 Further Evaluation of Mapping Ability -- 6 Conclusion -- References -- 12 Adaptation of Automatic Information Extraction Method for Environmental Heatmaps to U-Matrices of Self Organising Maps -- Abstract -- 1 Introduction -- 2 Problem Formulation -- 3 HInEx---Heatmap Information Extraction -- 3.1 The Idea -- 3.2 Heatmap Area Isolation -- 3.3 Clustering Image Pixels Based on Colors -- 3.4 Generating Tree Description -- 3.5 The Key Search and Its Analysis -- 3.6 The Axis Search and Their Analysis -- 3.7 Complete Heatmap Description -- 4 SOM Cluster Number Extraction Based on U-Matrix -- 4.1 The Idea of HInEx Application to SOM U-Matrix -- 4.2 Clustering -- 4.3 Extracting a U-Matrix Cell Corresponding to a Single Distance Between Neurons -- 4.4 Searching a Color Representing the Minimal Neuron Distance in SOM -- 4.5 Threshold-like Operation -- 4.6 Dilatation and Erosion-like Operations -- 4.7 Searching for the Number of Groups in SOM -- 5 SOM Generator Description -- 6 Experimental Study -- 7 Conclusion -- Acknowledgements -- References -- 13 Evolutionary Computing and Genetic Algorithms: Paradigm Applications in 3D Printing Process Optimization -- Abstract -- 1 Introduction -- 2 Evolutionary Optimization -- 3 Determination of the Pareto-Optimal Build Orientations in Stereolithography -- 3.1 Orientation Selection in SL -- 3.2 Algorithm Configuration and Implementation -- 3.3 Build Orientation Case Study. , 4 Determination of the Optimum Packing Layout in Stereolithography Machine Workspace -- 4.1 Optimization Scheme -- 4.2 Packing Layout Construction Process -- 4.3 Packing Layout Case Studies -- 5 Concluding Remarks -- References -- 14 Car-Like Mobile Robot Navigation: A Survey -- Abstract -- 1 Introduction -- 2 RRT-Based Methods -- 2.1 Unsafe Path Planning -- 2.2 Safe Path Planning -- 2.3 Rapidly Exploring Random Tree Algorithm on Rough Terrains (RRT-RT) -- 2.4 RRT Motion Planning Subsystem -- 2.5 Partial Motion Planning -- 2.6 Sensor-Based Random Tree (SRT) -- 2.7 RRT* Algorithm -- 2.8 Voronoi Fast Marching (VFM) and Fast Marching (FM2) -- 2.9 SBL Algorithm -- 2.10 Single-Query Motion Planning -- 2.11 Dynamic-Domain RRT -- 2.12 Transition-Based RRT -- 2.13 Parallelizing Rapidly-Exploring Random Tree (RRT) Algorithm on Large-Scale Distributed-Memory Architectures -- 2.14 Obstacle Sensitive Cost Function for Navigating Car-Like Robots -- 3 Methods Based on Fuzzy Logic -- 3.1 Distributed Active-Vision Network-Space System -- 3.2 Internet-Based Smart Space Navigation Using Fuzzy-Neural Adaptive Control -- 4 Sensor-Based Methods -- 4.1 Dynamic Window Approach (DWA) -- 4.2 Generalized Voronoi Graph (GVG) Theory -- 4.3 Navigation in Dynamic Environments Using Trajectory Deformation -- 4.4 Probabilistic Velocity Obstacle (PVO) -- 5 SLAM-Based Methods -- 5.1 On-line Path Following -- 5.2 The CyCab: A Car-Like Robot Navigating Autonomously and Safely Among Pedestrians -- 5.3 V-Slam -- 5.4 SLAM-Based Turning Strategy in Restricted Environments -- 5.5 L-Slam -- 6 Conclusions and Future Work -- 6.1 Future Directions in Autonomous Robot Navigation and Obstacle Perception -- 6.2 Future Directions in Applications of Autonomously-Navigating Robots -- References -- 15 Computing a Similarity Coefficient for Mining Massive Data Sets -- Abstract -- 1 Introduction. , 2 Related Work.

Note: Intro -- Contents -- Part I: Introduction -- Chapter 1: Ecological Informatics: An Introduction -- 1.1 Introduction -- 1.2 Data Management -- 1.3 Analysis and Synthesis -- 1.4 Communicating and Informing Decisions -- 1.5 Case Studies -- References -- Part II: Managing Ecological Data -- Chapter 2: Project Data Management Planning -- 2.1 Introduction -- 2.2 Components of a Data Management Plan -- 2.2.1 Context -- 2.2.2 Data Collection and Acquisition -- 2.2.3 Data Organization -- 2.2.4 Quality Assurance/Quality Control -- 2.2.5 Documentation -- 2.2.6 Storage and Preservation -- 2.2.7 Data Integration, Analysis, Modeling and Visualization -- 2.2.8 Data Policies -- Box 2.1 Recommended Data Citation Guidelines from Dryad Digital Repository (2016) -- 2.2.9 Communication and Dissemination of Research Outputs -- 2.2.10 Roles and Responsibilities -- 2.2.11 Budget -- 2.3 Developing and Using a Data Management Plan -- 2.3.1 Best Practices for Creating the Plan -- 2.3.2 Using the Plan -- 2.4 Conclusion -- References -- Chapter 3: Scientific Databases for Environmental Research -- 3.1 Introduction -- 3.2 Challenges for Scientific Databases -- 3.3 Examples of Scientific Databases -- 3.3.1 A Useful Analogy -- 3.3.2 Examples of Databases -- 3.4 Evolving a Database -- 3.4.1 A Strategy for Evolving a Database -- 3.4.2 Choosing Software -- 3.4.3 Database Management System (DBMS) Types -- 3.4.4 Data Models and Normalization -- 3.4.5 Advantages and Disadvantages of Using a DBMS -- 3.5 Interlinking Information Resources -- 3.5.1 A Database Related to the Human Genome Project -- 3.5.2 Environmental Databases for Sharing Data -- 3.5.3 Tools for Interlinking Information -- 3.6 Conclusions -- References -- Chapter 4: Quality Assurance and Quality Control (QA/QC) -- 4.1 Introduction -- 4.2 Quality Assurance -- 4.3 Quality Control -- 4.3.1 Data Filters. , 4.3.2 Graphical QC -- 4.3.3 Statistical QC -- 4.3.4 Treatment of Errors and Outliers -- 4.4 Implementing QA/QC -- 4.5 Conclusion -- References -- Chapter 5: Creating and Managing Metadata -- 5.1 Introduction -- 5.2 Metadata Descriptors -- 5.3 Metadata Standards -- 5.3.1 Dublin Core Metadata Initiative -- 5.3.2 Darwin Core -- 5.3.3 Ecological Metadata Language -- 5.3.4 GBIF Metadata Profile -- 5.3.5 FGDC CSDGM -- 5.3.6 ISO 19115 -- 5.4 Metadata Management -- 5.4.1 Metadata Tools -- 5.4.2 Best Practices for Creating and Managing Metadata -- 5.5 Conclusion -- References -- Chapter 6: Preserve: Protecting Data for Long-Term Use -- 6.1 Introduction -- 6.1.1 Preservation and Its Benefits -- 6.2 Practices for Preserving Ecological Data -- 6.2.1 Define the Contents of Your Data Files -- 6.2.2 Define the Parameters -- 6.2.3 Use Consistent Data Organization -- 6.2.4 Use Stable File Formats -- 6.2.5 Specify Spatial Information -- 6.2.6 Assign Descriptive File Names -- 6.2.7 Document Processing Information -- 6.2.8 Perform Quality Assurance -- 6.2.9 Provide Documentation -- 6.2.10 Protect Your Data -- 6.3 Prepare Your Data for Archival -- 6.4 What the Archive Does -- 6.4.1 Quality Assurance -- 6.4.2 Documentation and Metadata -- 6.4.3 Release of a Data Set -- 6.5 Data Users -- 6.6 Conclusions -- Appendix: Example R-Script for Processing Data -- References -- Chapter 7: Data Discovery -- 7.1 Introduction -- 7.2 Discovering Data Created by Others -- 7.2.1 Internet Search Engines -- 7.2.2 Data Repositories -- 7.2.3 Data Directories -- 7.2.4 Data Aggregators -- 7.3 Best Practices for Promoting Data Discovery and Reuse -- 7.3.1 Data Products -- Box 7.1 DataCite Recommendations for Data Citation -- Box 7.2 Dryad Digital Repository Data Citation Recommendations -- 7.3.2 Scientific Code -- References -- Chapter 8: Data Integration: Principles and Practice. , 8.1 Introduction -- 8.2 Essential Characteristics of All Data -- 8.3 Data as Records About Reality -- 8.4 Record-Keeping and Prose Documents as Data Integration Challenges -- 8.5 Formal Data Structures Facilitate Integration -- 8.5.1 Sets and Sequences -- 8.5.2 Matrices -- 8.5.3 Cross-classifications -- 8.5.4 Tables -- 8.5.5 Tables or Spreadsheets? -- 8.5.6 Tables or Cross-classifications? -- 8.5.7 Modeling True Tables -- 8.5.8 Need for Global Keys -- 8.6 Merging or JOINing Tables -- 8.6.1 APPENDING or Unioning -- 8.6.2 JOINs -- 8.7 The Datum Is the Atom -- 8.8 Conclusion -- References -- Part III: Analysis, Synthesis and Forecasting of Ecological Data -- Chapter 9: Inferential Modelling of Population Dynamics -- 9.1 Introduction -- 9.2 Inferential Modelling of Ecological Data by the Hybrid Evolutionary Algorithm -- 9.2.1 Population Dynamics of the Cyanobacterium Microcystis in Lake Müggelsee (Germany) -- 9.2.2 Meta-Analysis of Population Dynamics of the Cyanobacterium -- 9.3 Inferential Modelling of Ecological Data by Regression Trees -- 9.3.1 Induction Algorithm of Regression Trees -- 9.3.2 Pruning of Regression Trees -- 9.3.3 Diatom Populations in Lake Prespa (Mazedonia) -- 9.3.4 Vegetation Status of Selected Land Sites in Victoria (Australia) -- 9.4 Conclusions -- References -- Chapter 10: Process-Based Modeling of Nutrient Cycles and Food-Web Dynamics -- 10.1 Introduction -- 10.2 Zero- and One-Dimensional Lake Models -- 10.2.1 Zero-Dimensional Model for the Phosphorus Cycle in a Hypereutrophic Wetland -- 10.2.2 One-Dimensional Model for Nutrient Cycles and Plankton Dynamics in Lakes and Reservoirs -- 10.3 Multi-dimensional Lake Models -- 10.3.1 Horizontal and Vertical Transport of Nutrients and Organisms -- 10.3.2 Multi-segment Lake Model for Studying Dreissenids and Macrophytes -- 10.4 Concluding Remarks -- References. , Chapter 11: Uncertainty Analysis by Bayesian Inference -- 11.1 Does Uncertainty Really Matter? -- 11.2 Hamilton Harbour -- 11.2.1 Introduction -- 11.2.2 Eutrophication Modeling to Elucidate the Role of Lower Food Web -- 11.2.3 Nutrient Export Modeling for the Hamilton Harbour Watershed -- 11.3 Bay of Quinte -- 11.3.1 Introduction -- 11.3.2 Modeling the Relationship Among Watershed Physiography, Land Use Patterns, and Phosphorus Loading -- 11.3.3 Eutrophication Risk Assessment with Process-Based Modeling and Determination of Water Quality Criteria -- 11.4 Concluding Remarks -- References -- Chapter 12: Multivariate Data Analysis by Means of Self-Organizing Maps -- 12.1 Introduction -- 12.2 Properties of a Self-Organizing Map -- 12.3 Data Preparation -- 12.3.1 Missing Values and Outliers -- 12.3.2 Data Transformation -- 12.3.3 Distance Measure -- 12.4 Self-Organizing Maps -- 12.4.1 Architecture -- 12.4.2 Learning Algorithm -- Box 12.1 Sequential Learning Algorithm of an SOM -- 12.4.3 Evaluation of Trained Map Quality -- 12.4.4 Optimum Map Size -- 12.4.5 Clustering SOM Units -- 12.4.6 Evaluation of Input Variables -- 12.4.7 Relations Between Biological and Environmental Variables -- 12.5 Application in Ecological Modelling -- 12.6 SOM Tools -- 12.7 Example of SOM Application -- 12.8 Advantages and Disadvantages -- 12.8.1 Utility for Training and Information Extraction -- 12.8.2 Visualization and Recognition -- 12.8.3 Architecture Flexibility -- 12.8.4 Flexibility in Combining with Other Models -- 12.8.5 Constraints on Measure Consistency and Output Variability -- 12.8.6 Necessity of Sufficient Data -- 12.9 Future Development -- 12.10 Conclusions -- References -- Chapter 13: GIS-Based Data Synthesis and Visualization -- 13.1 Introduction -- 13.2 Synthesizing Species Distributions by Virtual Species. , 13.3 Cartograms to Synthesize and Visualize Sampling Effort Bias -- 13.4 Fuzzy Methods to Synthesize Species Distribution Uncertainty -- 13.5 Synthesis of Remote Sensing Data -- 13.5.1 Exploratory Data Analysis -- 13.5.1.1 Correlation of Remotely Sensed Bands by Hexagon Binning -- 13.5.1.2 Correlation Among Several Layers by Texture Measures -- 13.5.2 Fourier Transformations -- 13.6 Synthesizing Diversity Measurements from Space: The Case of Generalized Entropy -- 13.7 Neutral Landscapes -- 13.8 Conclusions -- References -- Part IV: Communicating and Informing Decisions -- Chapter 14: Communicating and Disseminating Research Findings -- 14.1 Introduction -- 14.2 Publishing Research Findings -- 14.2.1 Scholarly Publications -- 14.2.1.1 Journal Articles -- 14.2.1.2 Abstracts -- 14.2.1.3 Technical Reports -- 14.2.1.4 Books and Book Chapters -- 14.2.2 Newspaper and Magazine Articles for General Audiences -- 14.2.3 Designing Effective Figures -- 14.3 Communicating Research Findings Outside of Publications -- 14.3.1 Simple Steps for Giving an Effective Presentation -- 14.3.2 Best Practices for Slides -- 14.3.2.1 Slide Design -- 14.3.2.2 Text Slides -- 14.3.2.3 Graphics -- 14.3.3 Handouts -- 14.3.4 Posters -- 14.4 Communication in a Virtual Environment -- 14.4.1 Websites -- 14.4.2 Types and Uses of Different Social Media -- 14.4.3 Simple Steps for Effective Use of Social Media -- 14.4.4 Understanding Your Social Media Impact -- 14.5 Metrics and Altmetrics -- 14.6 Conclusion -- References -- Chapter 15: Operational Forecasting in Ecology by Inferential Models and Remote Sensing -- 15.1 Introduction -- 15.2 Early Warning of HABs Based on Inferential Modelling -- 15.2.1 Cyanobacterium Cylindrospermopsis in Lake Wivenhoe (Australia) -- 15.2.2 Cyanotoxin Microcystins in Lake Vaal (South Africa) -- 15.3 Early Warning of HABs Based on Remotely-Sensed Data. , 15.3.1 Earth Observation of Water Quality Parameters.